Probably the most Insightful Stories About Deepseek V3 - Medium > 자유게시판

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Multiple estimates put DeepSeek within the 20K (on ChinaTalk) to 50K (Dylan Patel) A100 equivalent of GPUs. Training one model for a number of months is extraordinarily dangerous in allocating an organization’s most useful assets - the GPUs. A real value of possession of the GPUs - to be clear, we don’t know if DeepSeek owns or rents the GPUs - would follow an analysis just like the SemiAnalysis total cost of possession mannequin (paid characteristic on top of the publication) that incorporates prices along with the actual GPUs. The total compute used for the DeepSeek V3 mannequin for pretraining experiments would probably be 2-4 instances the reported quantity within the paper. The cumulative question of how much whole compute is utilized in experimentation for a model like this is much trickier. We’ll get into the precise numbers below, however the query is, which of the many technical improvements listed in the deepseek ai V3 report contributed most to its studying efficiency - i.e. mannequin efficiency relative to compute used. This may enable us to construct the next iteration of DEEPSEEK to suit the particular wants of agricultural companies corresponding to yours.

Now that we all know they exist, many groups will build what OpenAI did with 1/tenth the cost. And there is a few incentive to proceed putting things out in open source, but it's going to obviously become increasingly aggressive as the price of this stuff goes up. Most of the strategies DeepSeek describes in their paper are issues that our OLMo staff at Ai2 would profit from gaining access to and is taking direct inspiration from. For one example, consider comparing how the DeepSeek V3 paper has 139 technical authors. Given the above greatest practices on how to offer the model its context, and the prompt engineering techniques that the authors advised have positive outcomes on consequence. Why this matters - asymmetric warfare comes to the ocean: "Overall, the challenges offered at MaCVi 2025 featured strong entries throughout the board, pushing the boundaries of what is feasible in maritime vision in several different elements," the authors write. Drawing on intensive safety and intelligence experience and advanced analytical capabilities, DeepSeek arms decisionmakers with accessible intelligence and insights that empower them to grab alternatives earlier, anticipate dangers, and strategize to satisfy a variety of challenges. Using compute benchmarks, nonetheless, especially in the context of national safety risks, is somewhat arbitrary.

Before we begin, we would like to say that there are a large amount of proprietary "AI as a Service" corporations corresponding to chatgpt, claude and so on. We solely need to use datasets that we will obtain and run domestically, no black magic. However, to resolve advanced proofs, these models need to be fine-tuned on curated datasets of formal proof languages. The prices to practice fashions will proceed to fall with open weight fashions, especially when accompanied by detailed technical reports, however the tempo of diffusion is bottlenecked by the necessity for challenging reverse engineering / reproduction efforts. This post revisits the technical details of DeepSeek V3, but focuses on how best to view the fee of training models on the frontier of AI and the way these costs could also be altering. These prices usually are not essentially all borne immediately by DeepSeek, i.e. they might be working with a cloud provider, but their value on compute alone (earlier than something like electricity) is at least $100M’s per yr. The CapEx on the GPUs themselves, not less than for H100s, is probably over $1B (primarily based on a market worth of $30K for a single H100). 16,000 graphics processing items (GPUs), if no more, DeepSeek claims to have needed only about 2,000 GPUs, specifically the H800 series chip from Nvidia.

For reference, the Nvidia H800 is a "nerfed" version of the H100 chip. For Chinese corporations which are feeling the pressure of substantial chip export controls, it can't be seen as significantly surprising to have the angle be "Wow we are able to do means greater than you with much less." I’d most likely do the identical of their footwear, it is much more motivating than "my cluster is greater than yours." This goes to say that we want to know how essential the narrative of compute numbers is to their reporting. The fact that the model of this high quality is distilled from DeepSeek’s reasoning model series, R1, makes me extra optimistic in regards to the reasoning mannequin being the true deal. Among the noteworthy enhancements in DeepSeek’s training stack embody the next. DeepSeek applied many methods to optimize their stack that has only been completed well at 3-5 different AI laboratories in the world. Reproducing this is not unattainable and bodes nicely for a future where AI skill is distributed across more players. The put up-training side is much less revolutionary, but provides extra credence to those optimizing for online RL training as DeepSeek did this (with a type of Constitutional AI, as pioneered by Anthropic)4.

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